OpenRaiser/Intern-Atlas huggingface dataset:
AI Behind Closed Doors: a Primer on The Governance of Internal Deployment
Venue: arXiv cs.CY 2025
Year: 2025

The most advanced future AI systems will first be deployed inside the frontier AI companies developing them. According to these companies and independent experts, AI systems may reach or even surpass human intelligence and capabilities by 2030. Internal deployment is, therefore, a key source of benefits and risks from frontier AI systems. Despite this, the governance of the internal deployment of highly advanced frontier AI systems appears absent. This report aims to address this absence by priming a conversation around the governance of internal deployment. It presents a conceptualization of internal deployment, learnings from other sectors, reviews of existing legal frameworks and their applicability, and illustrative examples of the type of scenarios we are most concerned about. Specifically, it discusses the risks correlated to the loss of control via the internal application of a misaligned AI system to the AI research and development pipeline, and unconstrained and undetected power concentration behind closed doors. The report culminates with a small number of targeted recommendations that provide a first blueprint for the governance of internal deployment.
Domains, Information Frames, and Their Logic
Venue: arXiv cs.LO 2025
Year: 2025

In \cite{sp25}, continuous information frames were introduced that capture exactly all continuous domains. They are obtained from the information frames considered in \cite{sp21} by omitting the conservativity requirement. Information frames generalise Scott's information systems~\cite{sc82}: Instead of the global consistency predicate, there is now a local consistency predicate for each token. Strong information frames are obtained by strengthening the conditions for these predicates. Let $\CIF$ and $\SIF$ be the corresponding categories. In \cite{sxx08} another generalisation of Scott's information systems was introduced which also exactly captures all continuous domains. As shown in \cite{hzl15}, the definition can be simplified while maintaining the representation result. Let $\CIS$ and $\SCIS$ be the corresponding categories. It is shown that all these categories are equivalent. Moreover, the equivalence extends to the subcategories of (strong) continuous information frames with truth elements. Such information frames capture exactly all pointed continuous domains. Continuous information frames are families of rudimentary logics, associated with each token is a local consistency predicate and an entailment relation. However, they lack the expressive power of propositional logic. In an attempt to make each of this logics more expressible, continuous stratified conjunctive logics are introduced. These are families of conjunctive logics. The category $\CSL$ of such logics is shown to be isomorphic to $\SIF_{\bt}$, the category of strong continuous information frames with a truth element.
Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks
Venue: arXiv cs.NI 2025
Year: 2025

Decentralized federated learning (DFL) is a promising machine learning paradigm for bringing artificial intelligence (AI) capabilities to the network edge. Running DFL on top of edge networks, however, faces severe performance challenges due to the extensive parameter exchanges between agents. Most existing solutions for these challenges were based on simplistic communication models, which cannot capture the case of learning over a multi-hop bandwidth-limited network. In this work, we address this problem by jointly designing the communication scheme for the overlay network formed by the agents and the mixing matrix that controls the communication demands between the agents. By carefully analyzing the properties of our problem, we cast each design problem into a tractable optimization and develop an efficient algorithm with guaranteed performance. Our evaluations based on real topology and data show that the proposed algorithm can reduce the total training time by over $80\%$ compared to the baseline without sacrificing accuracy, while significantly improving the computational efficiency over the state of the art.
An Evaluation of N-Gram Selection Strategies for Regular Expression Indexing in Contemporary Text Analysis Tasks. Extended Version
Venue: arXiv cs.DB 2025
Year: 2025

Efficient evaluation of regular expressions (regex, for short) is crucial for text analysis, and n-gram indexes are fundamental to achieving fast regex evaluation performance. However, these indexes face scalability challenges because of the exponential number of possible n-grams that must be indexed. Many existing selection strategies, developed decades ago, have not been rigorously evaluated on contemporary large-scale workloads and lack comprehensive performance comparisons. Therefore, a unified and comprehensive evaluation framework is necessary to compare these methods under the same experimental settings. This paper presents the first systematic evaluation of three representative n-gram selection strategies across five workloads, including real-time production logs and genomic sequence analysis. We examine their trade-offs in terms of index construction time, storage overhead, false positive rates, and end-to-end query performance. Through empirical results, this study provides a modern perspective on existing n-gram based regular expression evaluation methods, extensive observations, valuable discoveries, and an adaptable testing framework to guide future research in this domain. We make our implementations of these methods and our test framework available as open-source at https://github.com/mush-zhang/RegexIndexComparison.
Kernels for Storage Capacity and Dual Index Coding
Venue: arXiv cs.DS 2025
Year: 2025

The storage capacity of a graph measures the maximum amount of information that can be stored across its vertices, such that the information at any vertex can be recovered from the information stored at its neighborhood. The study of this graph quantity is motivated by applications in distributed storage and by its intimate relations to the index coding problem from the area of network information theory. In the latter, one wishes to minimize the amount of information that has to be transmitted to a collection of receivers, in a way that enables each of them to discover its required data using some prior side information. In this paper, we initiate the study of the Storage Capacity and Index Coding problems from the perspective of parameterized complexity. We prove that the Storage Capacity problem parameterized by the solution size admits a kernelization algorithm producing kernels of linear size. We also provide such a result for the Index Coding problem, in the linear and non-linear settings, where it is parameterized by the dual value of the solution, i.e., the length of the transmission that can be saved using the side information. A key ingredient in the proofs is the crown decomposition technique due to Chor, Fellows, and Juedes (WG 2003, WG 2004). As an application, we significantly extend an algorithmic result of Dau, Skachek, and Chee (IEEE Trans. Inform. Theory, 2014).
Reachability in Geometrically $d$-Dimensional VASS
Venue: arXiv cs.CC 2025
Year: 2025

Reachability of vector addition systems with states (VASS) is Ackermann complete~\cite{leroux2021reachability,czerwinski2021reachability}. For $d$-dimensional VASS reachability it is known that the problem is NP-complete~\cite{HaaseKreutzerOuaknineWorrell2009} when $d=1$, PSPACE-complete~\cite{BlondinFinkelGoellerHaaseMcKenzie2015} when $d=2$, and in $\mathbf{F}_d$~\cite{FuYangZheng2024} when $d>2$. A geometrically $d$-dimensional VASS is a $D$-dimensional VASS for some $D\ge d$ such that the space spanned by the displacements of the circular paths admitted in the $D$-dimensional VASS is $d$-dimensional. It is proved that the $\mathbf{F}_d$ upper bounds remain valid for the reachability problem in the geometrically $d$-dimensional VASSes with $d>2$.
Socrates or Smartypants: Testing Logic Reasoning Capabilities of Large Language Models with Logic Programming-based Test Oracles
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have achieved significant progress in language understanding and reasoning. Evaluating and analyzing their logical reasoning abilities has therefore become essential. However, existing datasets and benchmarks are often limited to overly simplistic, unnatural, or contextually constrained examples. In response to the growing demand, we introduce SmartyPat-Bench, a challenging, naturally expressed, and systematically labeled benchmark derived from real-world high-quality Reddit posts containing subtle logical fallacies. Unlike existing datasets and benchmarks, it provides more detailed annotations of logical fallacies and features more diverse data. To further scale up the study and address the limitations of manual data collection and labeling - such as fallacy-type imbalance and labor-intensive annotation - we introduce SmartyPat, an automated framework powered by logic programming-based oracles. SmartyPat utilizes Prolog rules to systematically generate logically fallacious statements, which are then refined into fluent natural-language sentences by LLMs, ensuring precise fallacy representation. Extensive evaluation demonstrates that SmartyPat produces fallacies comparable in subtlety and quality to human-generated content and significantly outperforms baseline methods. Finally, experiments reveal nuanced insights into LLM capabilities, highlighting that while excessive reasoning steps hinder fallacy detection accuracy, structured reasoning enhances fallacy categorization performance.
How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension
Venue: arXiv cs.CL 2025
Year: 2025

Large language models are increasingly used in scientific domains, especially for molecular understanding and analysis. However, existing models are affected by hallucination issues, resulting in errors in drug design and utilization. In this paper, we first analyze the sources of hallucination in LLMs for molecular comprehension tasks, specifically the knowledge shortcut phenomenon observed in the PubChem dataset. To evaluate hallucination in molecular comprehension tasks with computational efficiency, we introduce \textbf{Mol-Hallu}, a novel free-form evaluation metric that quantifies the degree of hallucination based on the scientific entailment relationship between generated text and actual molecular properties. Utilizing the Mol-Hallu metric, we reassess and analyze the extent of hallucination in various LLMs performing molecular comprehension tasks. Furthermore, the Hallucination Reduction Post-processing stage~(HRPP) is proposed to alleviate molecular hallucinations, Experiments show the effectiveness of HRPP on decoder-only and encoder-decoder molecular LLMs. Our findings provide critical insights into mitigating hallucination and improving the reliability of LLMs in scientific applications.
Capybara-OMNI: An Efficient Paradigm for Building Omni-Modal Language Models
Venue: arXiv cs.CL 2025
Year: 2025

With the development of Multimodal Large Language Models (MLLMs), numerous outstanding accomplishments have emerged within the open-source community. Due to the complexity of creating and training multimodal data pairs, it is still a computational and time-consuming process to build powerful MLLMs. In this work, we introduce Capybara-OMNI, an MLLM that trains in a lightweight and efficient manner and supports understanding text, image, video, and audio modalities. We present in detail the framework design, the data construction, and the training recipe, to develop an MLLM step-by-step to obtain competitive performance. We also provide exclusive benchmarks utilized in our experiments to show how to properly verify understanding capabilities across different modalities. Results show that by following our guidance, we can efficiently build an MLLM that achieves competitive performance among models of the same scale on various multimodal benchmarks. Additionally, to enhance the multimodal instruction following and conversational capabilities of the model, we further discuss how to train the chat version upon an MLLM understanding model, which is more in line with user habits for tasks like real-time interaction with humans. We publicly disclose the Capybara-OMNI model, along with its chat-based version. The disclosure includes both the model weights, a portion of the training data, and the inference codes, which are made available on GitHub.
You've Changed: Detecting Modification of Black-Box Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are often provided as a service via an API, making it challenging for developers to detect changes in their behavior. We present an approach to monitor LLMs for changes by comparing the distributions of linguistic and psycholinguistic features of generated text. Our method uses a statistical test to determine whether the distributions of features from two samples of text are equivalent, allowing developers to identify when an LLM has changed. We demonstrate the effectiveness of our approach using five OpenAI completion models and Meta's Llama 3 70B chat model. Our results show that simple text features coupled with a statistical test can distinguish between language models. We also explore the use of our approach to detect prompt injection attacks. Our work enables frequent LLM change monitoring and avoids computationally expensive benchmark evaluations.
CRAB: A Benchmark for Evaluating Curation of Retrieval-Augmented LLMs in Biomedicine
Venue: arXiv cs.CL 2025
Year: 2025

Recent development in Retrieval-Augmented Large Language Models (LLMs) have shown great promise in biomedical applications. How ever, a critical gap persists in reliably evaluating their curation ability the process by which models select and integrate relevant references while filtering out noise. To address this, we introduce the benchmark for Curation of Retrieval-Augmented LLMs in Biomedicine (CRAB), the first multilingual benchmark tailored for evaluating the biomedical curation of retrieval-augmented LLMs, available in English, French, German and Chinese. By incorporating a novel citation-based evaluation metric, CRAB quantifies the curation performance of retrieval-augmented LLMs in biomedicine. Experimental results reveal significant discrepancies in the curation performance of mainstream LLMs, underscoring the urgent need to improve it in the domain of biomedicine. Our dataset is available at https://huggingface.co/datasets/zhm0/CRAB.
Fast Parameter Optimization of Delayed Feedback Reservoir with Backpropagation and Gradient Descent
Venue: arXiv cs.AR 2025
Year: 2025

A delayed feedback reservoir (DFR) is a reservoir computing system well-suited for hardware implementations. However, achieving high accuracy in DFRs depends heavily on selecting appropriate hyperparameters. Conventionally, due to the presence of a non-linear circuit block in the DFR, the grid search has only been the preferred method, which is computationally intensive and time-consuming and thus performed offline. This paper presents a fast and accurate parameter optimization method for DFRs. To this end, we leverage the well-known backpropagation and gradient descent framework with the state-of-the-art DFR model for the first time to facilitate parameter optimization. We further propose a truncated backpropagation strategy applicable to the recursive dot-product reservoir representation to achieve the highest accuracy with reduced memory usage. With the proposed lightweight implementation, the computation time has been significantly reduced by up to 1/700 of the grid search.
Learning-based Delay Compensation for Enhanced Control of Assistive Soft Robots
Venue: arXiv cs.RO 2025
Year: 2025

Soft robots are increasingly used in healthcare, especially for assistive care, due to their inherent safety and adaptability. Controlling soft robots is challenging due to their nonlinear dynamics and the presence of time delays, especially in applications like a soft robotic arm for patient care. This paper presents a learning-based approach to approximate the nonlinear state predictor (Smith Predictor), aiming to improve tracking performance in a two-module soft robot arm with a short inherent input delay. The method uses Kernel Recursive Least Squares Tracker (KRLST) for online learning of the system dynamics and a Legendre Delay Network (LDN) to compress past input history for efficient delay compensation. Experimental results demonstrate significant improvement in tracking performance compared to a baseline model-based non-linear controller. Statistical analysis confirms the significance of the improvements. The method is computationally efficient and adaptable online, making it suitable for real-world scenarios and highlighting its potential for enabling safer and more accurate control of soft robots in assistive care applications.
PlanGlow: Personalized Study Planning with an Explainable and Controllable LLM-Driven System
Venue: arXiv cs.HC 2025
Year: 2025

Personal development through self-directed learning is essential in today's fast-changing world, but many learners struggle to manage it effectively. While AI tools like large language models (LLMs) have the potential for personalized learning planning, they face issues such as transparency and hallucinated information. To address this, we propose PlanGlow, an LLM-based system that generates personalized, well-structured study plans with clear explanations and controllability through user-centered interactions. Through mixed methods, we surveyed 28 participants and interviewed 10 before development, followed by a within-subject experiment with 24 participants to evaluate PlanGlow's performance, usability, controllability, and explainability against two baseline systems: a GPT-4o-based system and Khan Academy's Khanmigo. Results demonstrate that PlanGlow significantly improves usability, explainability, and controllability. Additionally, two educational experts assessed and confirmed the quality of the generated study plans. These findings highlight PlanGlow's potential to enhance personalized learning and address key challenges in self-directed learning.
M$^2$FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairness
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, fairness in machine learning has emerged as a critical concern to ensure that developed and deployed predictive models do not have disadvantageous predictions for marginalized groups. It is essential to mitigate discrimination against individuals based on protected attributes such as gender and race. In this work, we consider applying subgroup justice concepts to gradient-boosting machines designed for supervised learning problems. Our approach expanded gradient-boosting methodologies to explore a broader range of objective functions, which combines conventional losses such as the ones from classification and regression and a min-max fairness term. We study relevant theoretical properties of the solution of the min-max optimization problem. The optimization process explored the primal-dual problems at each boosting round. This generic framework can be adapted to diverse fairness concepts. The proposed min-max primal-dual gradient boosting algorithm was theoretically shown to converge under mild conditions and empirically shown to be a powerful and flexible approach to address binary and subgroup fairness.
Agentic AI Optimisation (AAIO): what it is, how it works, why it matters, and how to deal with it
Venue: arXiv cs.AI 2025
Year: 2025

The emergence of Agentic Artificial Intelligence (AAI) systems capable of independently initiating digital interactions necessitates a new optimisation paradigm designed explicitly for seamless agent-platform interactions. This article introduces Agentic AI Optimisation (AAIO) as an essential methodology for ensuring effective integration between websites and agentic AI systems. Like how Search Engine Optimisation (SEO) has shaped digital content discoverability, AAIO can define interactions between autonomous AI agents and online platforms. By examining the mutual interdependency between website optimisation and agentic AI success, the article highlights the virtuous cycle that AAIO can create. It further explores the governance, ethical, legal, and social implications (GELSI) of AAIO, emphasising the necessity of proactive regulatory frameworks to mitigate potential negative impacts. The article concludes by affirming AAIO's essential role as part of a fundamental digital infrastructure in the era of autonomous digital agents, advocating for equitable and inclusive access to its benefits.
Code Improvement Practices at Meta
Venue: arXiv cs.SE 2025
Year: 2025

The focus on rapid software delivery inevitably results in the accumulation of technical debt, which, in turn, affects quality and slows future development. Yet, companies with a long history of rapid delivery exist. Our primary aim is to discover how such companies manage to keep their codebases maintainable. Method: we investigate Meta's practices by collaborating with engineers on code quality and by analyzing rich source code change history to reveal a range of practices used for continual improvement of the codebase. In addition, we replicate several aspects of previous industry cases studies investigating the impact of code reengineering. Results: Code improvements at Meta range from completely organic grass-roots done at the initiative of individual engineers, to regularly blocked time and engagement via gamification of Better Engineering (BE) work, to major explicit initiatives aimed at reengineering the complex parts of the codebase or deleting accumulations of dead code. Over 14% of changes are explicitly devoted to code improvement and the developers are given ``badges'' to acknowledge the type of work and the amount of effort. Our investigation to prioritize which parts of the codebase to improve lead to the development of metrics to guide this decision making. Our analysis of the impact of reengineering activities revealed substantial improvements in quality and speed as well as a reduction in code complexity. Overall, such continual improvement is an effective way to develop software with rapid releases, while maintaining high quality.
Decision-based AI Visual Navigation for Cardiac Ultrasounds
Venue: arXiv cs.CV 2025
Year: 2025

Ultrasound imaging of the heart (echocardiography) is widely used to diagnose cardiac diseases. However, obtaining an echocardiogram requires an expert sonographer and a high-quality ultrasound imaging device, which are generally only available in hospitals. Recently, AI-based navigation models and algorithms have been used to aid novice sonographers in acquiring the standardized cardiac views necessary to visualize potential disease pathologies. These navigation systems typically rely on directional guidance to predict the necessary rotation of the ultrasound probe. This paper demonstrates a novel AI navigation system that builds on a decision model for identifying the inferior vena cava (IVC) of the heart. The decision model is trained offline using cardiac ultrasound videos and employs binary classification to determine whether the IVC is present in a given ultrasound video. The underlying model integrates a novel localization algorithm that leverages the learned feature representations to annotate the spatial location of the IVC in real-time. Our model demonstrates strong localization performance on traditional high-quality hospital ultrasound videos, as well as impressive zero-shot performance on lower-quality ultrasound videos from a more affordable Butterfly iQ handheld ultrasound machine. This capability facilitates the expansion of ultrasound diagnostics beyond hospital settings. Currently, the guidance system is undergoing clinical trials and is available on the Butterfly iQ app.